{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入包头"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib\n",
    "matplotlib.use('nbagg')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0 -5 -2 -2  2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x9aa0f98>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x92d77b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)#定一个种子\n",
    "x = np.arange(5)\n",
    "y = np.random.randint(-5,5,5)\n",
    "#y=np.random.randn(5)符合正态分布数字\n",
    "print (y)\n",
    "fig,axes = plt.subplots(ncols = 2)\n",
    "v_bars = axes[0].bar(x,y,color='red')\n",
    "h_bars = axes[1].barh(x,y,color='red')\n",
    "#添加基准线\n",
    "axes[0].axhline(0,color='grey',linewidth='3')\n",
    "axes[1].axvline(0,color='grey',linewidth='3')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如果能够描绘大于0和小于0颜色变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD8CAYAAABjAo9vAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAaAYwg4AxRB2ACiGsANAMYQdAIr5D2OTVRxtqk3PAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c06dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots()\n",
    "v_bars=axes.bar(x,y,color='lightblue')\n",
    "for bar,height in zip(v_bars,y):\n",
    "    if height>0 :\n",
    "        bar.set(edgecolor='darkred',color='red',linewidth='3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cumsum ：计算轴向元素累加和，返回由中间结果组成的数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.array([[1,2,3], [4,5,6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [5, 7, 9]], dtype=int32)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.cumsum(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  3,  6],\n",
       "       [ 4,  9, 15]], dtype=int32)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.cumsum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  3,  6, 10, 15, 21, 28, 36, 45], dtype=int32)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=np.arange(10)\n",
    "b.shape\n",
    "print(b)\n",
    "b.cumsum(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 条形图的填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#正态分布随机数\n",
    "x=np.random.randn(100).cumsum(0)\n",
    "y=np.linspace(0,10,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x9525358>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ALl26NMaPE5FFtVWUbG8Ermz2gD018GlcW6/PD+1kCc0sBn5dZdOs3ub9BWCcQM+ANwM/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x95257f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes=plt.subplots()\n",
    "axes.fill_between(x,y,color='lightblue')#填充图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### 填充区域问题2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x9437400>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HDO8e+Y+OrNDhXCLZ1niu56JFvFpTwzJgJNk5FnZXYALQDU/sS7NwzXRVAR/h552XZ/rF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa774048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,10,200)\n",
    "y1 = 2*x +1\n",
    "y2 = 3*x +1.2\n",
    "y_mean = 0.5*x*np.cos(2*x) + 2.5*x +1.1\n",
    "fig,axes=plt.subplots()\n",
    "axes.fill_between(x,y1,y2,color='red')# 填充一定要顺时针输入区域边图\n",
    "plt.plot(x,y_mean,color='darkred')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### figure上的显示限制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 5 artists>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa59d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(5)\n",
    "y = np.random.randint(-5,5,5)\n",
    "#y=np.random.randn(5)符合正态分布数字\n",
    "fig = plt.figure(figsize = (8,6))# figure表示x轴和y轴刻度的范围\n",
    "plt.barh(x,y,color='g',alpha = 0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### zip(a,b)\n",
    "zip()函数的定义\n",
    "从参数中的多个迭代器取元素组合成一个新的迭代器；\n",
    "返回：\n",
    "返回一个zip对象，其内部元素为元组；可以转化为列表或元组；\n",
    "传入参数：\n",
    "元组、列表、字典等迭代器。\n",
    "\n",
    "zip()函数的用法\n",
    "当zip()函数中只有一个参数时\n",
    "zip(iterable)从iterable中依次取一个元组，组成一个元组。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 如何柱状图上标注误差范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 0.5)\n",
      "4.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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bgGuSrK2qm2c0Pb+qTuyqDknS4LrsKRwKbKyq71fVQ8B5wFEdnk+StEhdhsK+wC3T1jc1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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f950b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_values = [1,2,3]\n",
    "variance = [0.2,0.4,0.5]\n",
    "bar_label = ['bar1','bar2','bar3']\n",
    "x_pos = list(range(len(bar_label)))\n",
    "plt.bar(x_pos,mean_values,yerr=variance,alpha=0.3) #yerr=varianc 设置的误差\n",
    "max_y = max(zip(mean_values,variance))\n",
    "print(max_y)\n",
    "print((max_y[0]+max_y[1])*1.2)#如何截取元素\n",
    "plt.ylim([0,(max_y[0]+max_y[1])*1.2])#显示y抽的大小\n",
    "plt.ylabel('variable y')\n",
    "plt.xticks(x_pos,bar_label)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1, 4)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5q68xvy+TpKqOJPmOJK/dzvprF/Squj7J/+rue5aeZa9U1b+vqo8n+e7M2kM/0/cledvS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbe4d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1 = np.array([1,2,3])\n",
    "x2 = np.array([2,2,3])\n",
    "bar_labels = ['bat1','bar2','bar3']\n",
    "#设置图形缩放比例\n",
    "fig = plt.figure(figsize = (8,6))\n",
    "y1=np.arange(len(x1))\n",
    "pos_y=[x for x in y1]\n",
    "plt.barh(pos_y,x1,color='g',alpha=0.3)\n",
    "plt.barh(pos_y,-x2,color='r',alpha=0.3)\n",
    "plt.ylabel('y label')\n",
    "plt.xlabel('x label')\n",
    "#调整x抽限制\n",
    "plt.xlim(-(max(x2))-1,max(x1)+1)\n",
    "plt.ylim(-1,len(x1)+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ##### 如何画竖直和水平的分割线\n",
    "    vlines(x, ymin, ymax)\n",
    "    hlines(y, xmin, xmax)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UgTN5kiRJBbLkSZIkFciSJ0mSVCDPyZMkqXAf+MAHckdQBpY8SZIK99nPfjZ3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\nJKlA/x+DVnVJBdJDzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd331b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data=range(200,225,5)\n",
    "bar_labels=list('abcde')\n",
    "fig=plt.figure(figsize=(10,8))\n",
    "y_pos=np.arange(len(data))\n",
    "y_pos=[x for x in y_pos]\n",
    "bars=plt.barh(y_pos,data,color='g',alpha=0.2)\n",
    "plt.yticks(y_pos,bar_labels,fontsize=16)\n",
    "#标出特殊分位线\n",
    "plt.vlines(min(data),min(y_pos)-1,max(y_pos)+1,linestyle='dashed')\n",
    "#标注text\n",
    "for b,d in zip(bars,data):\n",
    "     plt.text(b.get_width()+b.get_width()*0.05,b.get_y()+b.get_height()/2,'{0:.2%}'.format(d/min(data)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ScalarMappable使用【将通过渐进色进行填充】"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_values = range(10,18)\n",
    "x_pos = range(len(mean_values))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 8 artists>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.colors as col\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "#构建递进色图谱\n",
    "\n",
    "cmap1 = cm.ScalarMappable(col.Normalize(min(mean_values),max(mean_values),cm.hot)) #更具提供期望值进行热力图展示\n",
    "cmap2 = cm.ScalarMappable(col.Normalize(10,13,cm.hot))\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.subplot(121)\n",
    "plt.bar(x_pos,mean_values,color = cmap1.to_rgba(mean_values))\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(x_pos,mean_values,color = cmap2.to_rgba(mean_values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Mm/zK8pn3+9///ivOoO+++26zfJj6/nPzVsbY2Fhq+Xbq+9D33Xdf43179+69Yr86yXwc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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#简单了解\n",
    "patterns = ('-', '+', 'x', '\\\\', '*', 'o', 'O', '.')\n",
    "\n",
    "fig = plt.gca()\n",
    "\n",
    "mean_value = range(1,len(patterns)+1)\n",
    "x_pos = list(range(len(mean_value)))\n",
    "\n",
    "bars = plt.bar(x_pos,mean_value,color='white')\n",
    "\n",
    "for bar,pattern in zip(bars,patterns):\n",
    "    bar.set_hatch(pattern)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
